Use of routine HIV testing data for early detection of emerging HIV epidemics in high-risk subpopulations

A concept demonstration study

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)373-384
Number of pages12
JournalInfectious Disease Modelling
Volume3
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Demonstrations
HIV
Testing
Sampling
Concepts
Surveillance
Mathematical models
Infection
Acquired Immunodeficiency Syndrome
Selection Bias
Stochastic Simulation
Deterministic Model
Population
Saturation
Theoretical Models
Infrastructure
Monte Carlo Simulation
Mathematical Model
Metric

Keywords

  • Epidemiology
  • High-risk subpopulation
  • HIV
  • Mathematical modeling
  • Monte Carlo simulations
  • Sexually transmitted infection
  • Surveillance

ASJC Scopus subject areas

  • Infectious Diseases
  • Applied Mathematics
  • Health Policy

Cite this

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title = "Use of routine HIV testing data for early detection of emerging HIV epidemics in high-risk subpopulations: A concept demonstration study",
abstract = "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.",
keywords = "Epidemiology, High-risk subpopulation, HIV, Mathematical modeling, Monte Carlo simulations, Sexually transmitted infection, Surveillance",
author = "Houssein Ayoub and Susanne Awad and Laith Aburaddad",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/j.idm.2018.10.001",
language = "English",
volume = "3",
pages = "373--384",
journal = "Infectious Disease Modelling",
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TY - JOUR

T1 - Use of routine HIV testing data for early detection of emerging HIV epidemics in high-risk subpopulations

T2 - A concept demonstration study

AU - Ayoub, Houssein

AU - Awad, Susanne

AU - Aburaddad, Laith

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Epidemiology

KW - High-risk subpopulation

KW - HIV

KW - Mathematical modeling

KW - Monte Carlo simulations

KW - Sexually transmitted infection

KW - Surveillance

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U2 - 10.1016/j.idm.2018.10.001

DO - 10.1016/j.idm.2018.10.001

M3 - Article

VL - 3

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EP - 384

JO - Infectious Disease Modelling

JF - Infectious Disease Modelling

SN - 2468-0427

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