MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

Haoyi Wang; Chantal den Daas; Eline Op de Coul; Kai J. Jonas

doi:10.1016/j.sste.2023.100577

MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

Haoyi Wang, Chantal den Daas, Eline Op de Coul, Kai J. Jonas^* (Corresponding Author)

^*Corresponding author for this work

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Abstract

Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.

Original language	English
Article number	100577
Journal	Spatial and Spatio-temporal Epidemiology
Volume	45
Early online date	3 Feb 2023
DOIs	https://doi.org/10.1016/j.sste.2023.100577
Publication status	Published - Jun 2023

Bibliographical note

Acknowledgement:
We thank all participants of the surveys and the individuals involved in preparation, execution and analysis of the surveys. We thank Wim Zuilhof, Bouko Bakker, Aryanti Radyowijati, Koenraad Vermey, Arjan van Bijnen and the EMIS board for their invaluable help for the EMIS-2017 data. In addition, we thank John de Wit, Philippe Adam, and Wim Zuilhof, for their role in the development and collection of SMS-2018 data.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data Availability Statement

The authors do not have permission to share data

Keywords

Bayesian spatial analysis
HIV surveillance
MSM
Small area estimation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.sste.2023.100577Licence: CC BY

Wang_etal_SSTE_MSM_with_HIV_VOR
1877-5845/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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Cite this

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title = "MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands",

abstract = "Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.",

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note = "Acknowledgement: We thank all participants of the surveys and the individuals involved in preparation, execution and analysis of the surveys. We thank Wim Zuilhof, Bouko Bakker, Aryanti Radyowijati, Koenraad Vermey, Arjan van Bijnen and the EMIS board for their invaluable help for the EMIS-2017 data. In addition, we thank John de Wit, Philippe Adam, and Wim Zuilhof, for their role in the development and collection of SMS-2018 data. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.",

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N1 - Acknowledgement: We thank all participants of the surveys and the individuals involved in preparation, execution and analysis of the surveys. We thank Wim Zuilhof, Bouko Bakker, Aryanti Radyowijati, Koenraad Vermey, Arjan van Bijnen and the EMIS board for their invaluable help for the EMIS-2017 data. In addition, we thank John de Wit, Philippe Adam, and Wim Zuilhof, for their role in the development and collection of SMS-2018 data. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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N2 - Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.

AB - Despite close monitoring of HIV infections amongst MSM (MSMHIV), the true prevalence can be masked for areas with small population density or lack of data. This study investigated the feasibility of small area estimation with a Bayesian approach to improve HIV surveillance. Data from EMIS-2017 (Dutch subsample, n = 3,459) and the Dutch survey SMS-2018 (n = 5,653) were utilized. We applied a frequentist calculation to compare the observed relative risk of MSMHIV per Public Health Services (GGD) region in the Netherlands and a Bayesian spatial analysis and ecological regression to quantify how spatial heterogeneity in HIV amongst MSM is related to determinants while accounting for spatial dependence to obtain more robust estimates. Both estimations converged and confirmed that the prevalence is heterogenous across the Netherlands with some GGD regions having a higher-than-average risk. Our Bayesian spatial analysis to assess the risk of MSMHIV was able to close data gaps and provide more robust prevalence and risk estimations.

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MSM with HIV: Improving prevalence and risk estimates by a Bayesian small area estimation modelling approach for public health service areas in the Netherlands

Abstract

Bibliographical note

Data Availability Statement

Keywords

UN SDGs

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