Use of routine HIV testing data for early detection of emerging HIV epidemics in high-risk subpopulations: A concept demonstration study
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 emerging-epidemic 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 3.4–4.6) years after epidemic emergence using routine HIV testing, but 29.7 (95% CI 15.8–52.1) 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.
Other Information
Published in: Infectious Disease Modelling
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.idm.2018.10.001
Funding
Open Access funding provided by the Qatar National Library.
History
Language
- English
Publisher
ElsevierPublication Year
- 2018
License statement
This Item is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Institution affiliated with
- Qatar University
- College of Arts and Sciences - QU
- Weill Cornell Medicine - Qatar
- Hamad Bin Khalifa University
- College of Health and Life Sciences - HBKU