HIV estimates accuracy tool

tool

HIV remains one of the most important public health concerns in the European Union and European Economic Area. Accurate data are therefore crucial to appropriately direct and evaluate public health response.

The HIV estimates accuracy tool is a new end-user application that uses statistical methods to calculate adjusted estimates from HIV surveillance data taking into account the issues of missing data and reporting delay. The tool accepts case-based surveillance data for HIV containing a minimum required set of variables.
Version: 1.0.0

Access

The tool can be accessed through the following options:

1. For restricted users, the HIV estimates accuracy tool is a web tool available online through Shinyapps:

For new users:

To access the tool, send an e-mail to HIV.Modelling@ecdc.europa.eu with the subject: ’Registration for HIV estimates accuracy tool + Full Name’. An invitation e-mail will be sent back with a link to sign in to Shinyapps using one of the following three methods:

  • Shinyapps.io authentication (recommended)
  • Google authorization
  • GitHub authorization

After signing up, you will receive a second email from Shinyapps to log in to the tool using your credentials.

Image showing how to sign up to Shinyapps to access the HIV estimates accuracy too

For returning users:

    2. For experienced R users, a CRAN-like repository is set up for installing the tool as an R package in R GUI or RStudio.

    The tool can be installed using standard R commands executed in R console:

    3. An offline Windows x64 deployment package with R environment embedded

    The last option for local deployment of the tool is to use a deployment package, which includes all required software and R packages.

    • Download the deployment package (201 MB download size).
    • Unpack the file to a folder.
    • After unpacking, a new folder will appear called ’hivEstimatesAccuracy’. Double-click on the ’hivEstimatesAccuracy.bat’ file. This will open the tool in the default web browser. Close the browser window when finished with the file.

    This offline package can only run on 64-bit versions of Microsoft Windows 7, 8, and 10.

    Data integrity verification

    MD5 checksum: 325460fc68e349f1943d06a6b35cc7ed
    SHA1 checksum: a66a20772f30ba7bbc33132f3a36b9b84310c6cd

    Prerequisites

    For option 1 an active Internet connection is required. Relatively recent versions of web browsers such as Chrome, Firefox, Internet Explorer, Edge and Safari with support for JavaScript enabled are recommended.

    For option 2, both R engine and Pandoc must be installed, with LaTeX preferably installed as well. Option 3 (an offline Windows x64 deployment package) includes the required software and does not require additional prerequisites.

    Required software:

    a) R engine performs all calculations

    b) Pandoc converts Markdown documents (report sources) to HTML, LaTeX and/or Word. It is delivered with RStudio, so if one is already using RStudio, there is no need to install it separately.

    Optional software:

    c) LaTex has various alternatives, including Miktex, TexLive and TinyTex that generates PDF reports. If it is not installed, outputting the main report to PDF will fail.

    Background

    Missing data are a well recognised problem within surveillance systems. When values for certain variables are missing and cases with missing values are excluded from analysis, it may lead to biased and potentially less precise estimates. Reporting delay, the time from case diagnosis to notification, can lead to problems when analysing the most recent years given that information on certain cases or variables may not have been collected yet due to national reporting process characteristics. These phenomena are common in disease surveillance and also apply to HIV.

    The tool performs multiple imputations for the missing values of a set of variables (age, gender and CD4 count) using joint multivariate normal models and extensions or full conditional specification (also known as multiple imputation by chained equations). Additionally, the tool allows for correction of delays in reporting through reverse time hazard estimation. The adjustments may be used separately or in combination.

    The outputs include results in the form of a report containing tables and graphs, and datasets in various file formats, in which the corrections have been incorporated and are ready for further analysis.

    Data Preparation

    The tool accepts case-based surveillance data for HIV containing a minimum required set of variables. The support files types are rds., txt, csv, xls and xlsx (uncompressed and ZIP archives).

    Several attributes/variables are required by the tool to run the adjustments. The upload file must contain all these attribute/variable names. Different variable names are accepted, but the variables must be coded in a specific way.

    Manual

    A complete instruction manual explains the basics of the ECDC HIV estimates accuracy tool and includes technical and methodological details on how to use the tool and calculate estimates. The manual can also be consulted to interpret tool outputs and aid in the selection of certain parameters.

    Disclaimer

    ECDC accepts no responsibility or liability whatsoever (including but not limited to any direct or consequential loss or damage it might occur to you and/or any other third party) arising out of or in connection with the installation and/or usage of this software.
    Copyright © European Centre for Disease Prevention and Control, 2018.

    Suggested citation

    European Centre for Disease Prevention and Control. HIV estimates accuracy tool [Internet, software application]. Stockholm: EC DC; 2018. Available from: https://ecdc.europa.eu/en/publications-data/hiv-estimates-accuracy-tool

    Suggested literature

    Rosinska Magdalena, Pantazis Nikos, Janiec Janusz, Pharris Anastasia, Amato-Gauci Andrew J, Quinten Chantal, ECDC HIV/AIDS Surveillance Network. Potential adjustment methodology for missing data and reporting delay in the HIV Surveillance System, European Union/European Economic Area, 2015. EuroSurveillance 2018; 23(23).

    Support

    For technical support and reporting problems please contact: HIV.Modelling@ecdc.europa.eu.

    All updates on HIV/AIDS

    Event

    Pre-exposure prophylaxis in the EU/EEA. PrEP service delivery and monitoring: minimum standards and key principles

    15 Nov 2018 - 16 Nov 2018
    Stockholm, Sweden

    Data

    Surveillance systems overview for 2017

    table -

    Publication

    Public health guidance in brief on HIV, hepatitis B and C testing in the EU/EEA

    public health guidance -

    Data

    Infographic: HIV and AIDS in Europe 2017

    infographic -

    Data

    Reported HIV transmission modes in the EU/EEA 2017

    map -