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Title: Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers

Abstract

Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between immigration and diagnosis for foreign-born persons. We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CImore » 10.3-11.4%) of all PLHIV. Here, the proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90–90-90 UNAIDS target.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [4]; ORCiD logo [2];  [5];  [4]
  1. Stockholm Univ., Stockholm (Sweden); Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. Medical Center Rotterdam, Rotterdam (The Netherlands)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Public Health Agency of Sweden, Solna (Sweden)
  4. Karolinska Institute, Stockholm (Sweden); Karolinska Univ. Hospital, Stockholm (Sweden)
  5. Stockholm Univ., Stockholm (Sweden)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Institutes of Health (NIH); USDOE
OSTI Identifier:
1532732
Report Number(s):
LA-UR-19-25855
Journal ID: ISSN 0300-5771
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Epidemiology
Additional Journal Information:
Journal Name: International Journal of Epidemiology; Journal ID: ISSN 0300-5771
Country of Publication:
United States
Language:
English
Subject:
Biological Science; HIV-1; incidence estimation; undiagnosed HIV-1 infections; BED assay; pol sequences

Citation Formats

Giardina, Federica, Romero-Severson, Ethan O., Axelsson, Maria, Svedhem, Veronica, Leitner, Thomas Kenneth, Britton, Tom, and Albert, Jan. Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers. United States: N. p., 2019. Web. doi:10.1093/ije/dyz100.
Giardina, Federica, Romero-Severson, Ethan O., Axelsson, Maria, Svedhem, Veronica, Leitner, Thomas Kenneth, Britton, Tom, & Albert, Jan. Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers. United States. doi:10.1093/ije/dyz100.
Giardina, Federica, Romero-Severson, Ethan O., Axelsson, Maria, Svedhem, Veronica, Leitner, Thomas Kenneth, Britton, Tom, and Albert, Jan. Fri . "Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers". United States. doi:10.1093/ije/dyz100. https://www.osti.gov/servlets/purl/1532732.
@article{osti_1532732,
title = {Getting more from heterogeneous HIV-1 surveillance data in a high immigration country: estimation of incidence and undiagnosed population size using multiple biomarkers},
author = {Giardina, Federica and Romero-Severson, Ethan O. and Axelsson, Maria and Svedhem, Veronica and Leitner, Thomas Kenneth and Britton, Tom and Albert, Jan},
abstractNote = {Most HIV infections originate from individuals who are undiagnosed and unaware of their infection. Estimation of this quantity from surveillance data is hard because there is incomplete knowledge about (i) the time between infection and diagnosis (TI) for the general population, and (ii) the time between immigration and diagnosis for foreign-born persons. We developed a new statistical method for estimating the incidence of HIV-1 and the number of undiagnosed people living with HIV (PLHIV), based on dynamic modelling of heterogeneous HIV-1 surveillance data. The methods consist of a Bayesian non-linear mixed effects model using multiple biomarkers to estimate TI of HIV-1-positive individuals, and a novel incidence estimator which distinguishes between endogenous and exogenous infections by modelling explicitly the probability that a foreign-born person was infected either before or after immigration. The incidence estimator allows for direct calculation of the number of undiagnosed persons. The new methodology is illustrated combining heterogeneous surveillance data from Sweden between 2003 and 2015. A leave-one-out cross-validation study showed that the multiple-biomarker model was more accurate than single biomarkers (mean absolute error 1.01 vs ≥1.95). We estimate that 816 [95% credible interval (CI) 775-865] PLHIV were undiagnosed in 2015, representing a proportion of 10.8% (95% CI 10.3-11.4%) of all PLHIV. Here, the proposed methodology will enhance the utility of standard surveillance data streams and will be useful to monitor progress towards and compliance with the 90–90-90 UNAIDS target.},
doi = {10.1093/ije/dyz100},
journal = {International Journal of Epidemiology},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {5}
}

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