Effective antiretroviral therapy, anal high-risk human papillomavirus, anal intraepithelial neoplasia and anal cancer: a systematic review and meta-analysis

Published: 12 Feb 2020 | Version 1 | DOI: 10.17632/vxbt7rc27j.1
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Description of this data

This dataset contains data evaluating the association of antiretroviral therapy, HIV plasma viral load and contemporary and nadir CD4+ T-cell count (exposures) and the following outcomes: outcomes: anal HR-HPV prevalence, incidence and persistence; cytology- or histology-confirmed anal squamous intraepithelial lesions [SIL]/anal intraepithelial neoplasia [AIN] prevalence, incidence, progression or regression, and anal cancer incidence among people living with HIV (PLHIV).
It includes raw data for outcomes and exposure, and the crude effect estimate, and the adjusted effect estimate provided in the published material, in additional to information on the any adjustment for confounding, where available.

Experiment data files

Latest version

  • Version 1

    2020-02-12

    Published: 2020-02-12

    DOI: 10.17632/vxbt7rc27j.1

    Cite this dataset

    Kelly, Helen (2020), “Effective antiretroviral therapy, anal high-risk human papillomavirus, anal intraepithelial neoplasia and anal cancer: a systematic review and meta-analysis ”, Mendeley Data, v1 http://dx.doi.org/10.17632/vxbt7rc27j.1

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Institutions

London School of Hygiene and Tropical Medicine, Institut Catala d' Oncologia

Categories

Anal Cancer, HIV/AIDS, Human Papillomavirus, Clinical Management in Antiretroviral Therapy

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?
You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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