Cargando…
Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool
OBJECTIVE: The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15–49 years) are derived by either fitting to seroprevalence surveillance and survey dat...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919234/ https://www.ncbi.nlm.nih.gov/pubmed/31385865 http://dx.doi.org/10.1097/QAD.0000000000002324 |
_version_ | 1783480732587393024 |
---|---|
author | Mahiane, Severin G. Marsh, Kimberly Glaubius, Robert Eaton, Jeffrey W. |
author_facet | Mahiane, Severin G. Marsh, Kimberly Glaubius, Robert Eaton, Jeffrey W. |
author_sort | Mahiane, Severin G. |
collection | PubMed |
description | OBJECTIVE: The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15–49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round. METHODS: Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the ‘r-logistic’ model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4(+) at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. RESULTS: We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4(+) at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10–0.45] in 1990 to about 0.77 (IQR: 0.50–0.89) in 2017. The mean CD4(+) at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224–567) in 1990 and 373 cells/μl (IQR: 174–475) by 2017. CONCLUSION: Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status. |
format | Online Article Text |
id | pubmed-6919234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-69192342020-03-10 Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool Mahiane, Severin G. Marsh, Kimberly Glaubius, Robert Eaton, Jeffrey W. AIDS Editorial OBJECTIVE: The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15–49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round. METHODS: Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the ‘r-logistic’ model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4(+) at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. RESULTS: We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4(+) at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10–0.45] in 1990 to about 0.77 (IQR: 0.50–0.89) in 2017. The mean CD4(+) at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224–567) in 1990 and 373 cells/μl (IQR: 174–475) by 2017. CONCLUSION: Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status. Lippincott Williams & Wilkins 2019-12-15 2019-08-02 /pmc/articles/PMC6919234/ /pubmed/31385865 http://dx.doi.org/10.1097/QAD.0000000000002324 Text en Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 |
spellingShingle | Editorial Mahiane, Severin G. Marsh, Kimberly Glaubius, Robert Eaton, Jeffrey W. Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title | Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title_full | Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title_fullStr | Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title_full_unstemmed | Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title_short | Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool |
title_sort | estimating and projecting the number of new hiv diagnoses and incidence in spectrum's case surveillance and vital registration tool |
topic | Editorial |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919234/ https://www.ncbi.nlm.nih.gov/pubmed/31385865 http://dx.doi.org/10.1097/QAD.0000000000002324 |
work_keys_str_mv | AT mahianesevering estimatingandprojectingthenumberofnewhivdiagnosesandincidenceinspectrumscasesurveillanceandvitalregistrationtool AT marshkimberly estimatingandprojectingthenumberofnewhivdiagnosesandincidenceinspectrumscasesurveillanceandvitalregistrationtool AT glaubiusrobert estimatingandprojectingthenumberofnewhivdiagnosesandincidenceinspectrumscasesurveillanceandvitalregistrationtool AT eatonjeffreyw estimatingandprojectingthenumberofnewhivdiagnosesandincidenceinspectrumscasesurveillanceandvitalregistrationtool |