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

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

doi:10.1016/j.sste.2023.100577

Cited by 6 publications

(15 citation statements)

References 24 publications

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“…In this study, we also explored whether COVID-19 vaccination uptake in the Netherlands was influenced by the selected regional socio-demographic characteristics as the spatial proxies on an ecological level. We, therefore, applied a spatio-temporal ecological regression modelling technique [16] which takes these spatial proxies into account to pick up additional associations and noises [21]. For the proportion of non-Western immigrants and the proportion of financially extremely disadvantaged individuals.…”

Section: Methodsmentioning

confidence: 99%

“…To investigate COVID-19 vaccination uptake on a small area level and to provide robust estimations Bayesian spatio-temporal analysis can be used. Bayesian spatio-temporal analysis is a well-established method for small-area-estimations [16][17][18][19][20][21]. Briefly, Bayesian spatiotemporal analysis can account for several sources of error or bias including spatial autocorrelation between neighbouring regions and proximity and time-dependent autocorrelation between consecutive periods in sparsely populated areas, compared to the observed frequentist prevalence calculation [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian spatio-temporal analysis is a well-established method for small-area-estimations [16][17][18][19][20][21]. Briefly, Bayesian spatiotemporal analysis can account for several sources of error or bias including spatial autocorrelation between neighbouring regions and proximity and time-dependent autocorrelation between consecutive periods in sparsely populated areas, compared to the observed frequentist prevalence calculation [20][21][22]. Despite Bayesian spatial and spatiotemporal analysis having been applied to monitor the distribution of COVID-19 infections in some countries, such as the United States [22], and the distribution of COVID-19 vaccination uptake, such as in Belgium [23], it has not been applied in the Netherlands for COVID-19 infections nor for vaccination uptake monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: A Bayesian ecological modelling analysis

Wang

Varol

Gültzow

et al. 2023

Preprint

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Background: In the transitioning era towards the COVID-19 endemic, there is still a sizable population that has never been vaccinated against COVID-19 in the Netherlands. To identify regions and populations that have a lower chance of vaccination uptake, this study provides a spatio-temporal estimation of the relative chance of COVID-19 vaccination uptake for the first, second, and the booster doses in the Netherlands on both municipality level and the public health services (regional) level. Methods: Data on COVID-19 vaccination uptake were retrieved from the publicly available national COVID-19 surveillance dataset. We used a Bayesian spatio-temporal modelling technique with the integrated nested Laplace approximation to account for the spatial structure and the space-time interaction. Additionally, we used an ecological regression modelling technique which takes into account areal level socio-demographic characteristics to adjust for their potential impact on the chance of the regional vaccination uptake. Results: Our findings revealed a heterogenous spatio-temporal distribution of the relative chance of COVID-19 vaccination uptake with highly overlapping trends of all three vaccination doses. Internal heterogeneity of COVID-19 vaccination uptake within one public health services region on the municipality level was also identified. The Dutch main urban area and the most religiously conservative regions were identified to have a lower-than-average chance of COVID-19 vaccination uptake compared to the rest of the country. Ecological regression modelling analysis revealed that regions with a higher proportion of non-Western immigrants had a lower chance of COVID-19 vaccination uptake for all vaccination scenarios. Conclusion: The obtained estimates should inform national and local COVID-19 vaccination policies and service strategies in the Netherlands for the ongoing COVID-19 campaign on the second booster. Namely, more regional efforts and services may be needed to close 'vaccination gaps' and optimise COVID-19 health-related outcomes, especially with regard to regions with a relatively higher proportion of marginalised populations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: A Bayesian ecological modelling analysis

Wang

Varol

Gültzow

et al. 2023

Preprint

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“…Bayesian spatial analysis was applied to estimate the regional posterior relative risk (RR) of newly diagnosed HIV among MSM in 2017 using selfreported data from EMIS-2017. Given the robust estimations using survey data by the Bayesian spatial analysis [15], we assumed the posterior RRs are comparable to the true RR. We then calculated the nationwide HIV incidence in 2017, using the newly diagnosed HIV surveillance unadjusted data among MSM from Santé Publique France as a numerator, and the estimated total HIV-negative MSM population in France by Ndawinz et al [25] as a denominator.…”

Section: Bayesian Spatio-temporal Analysismentioning

confidence: 99%

“…These psychosocial and behavioural determinants can differ regionally, too. Such joint modelling has shown to improve the robustness of estimates [14,15].…”

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal changes in pre‐exposure prophylaxis uptake among MSM in mainland France between 2016 and 2021: a Bayesian small area approach with MSM population estimation

et al. 2023

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Introduction In France, oral pre‐exposure prophylaxis (PrEP) for HIV prevention has been publicly available since 2016, mainly targeting at men who have sex with men (MSM). Reliable and robust estimations of the actual PrEP uptake among MSM on a localized level can provide additional insights to identify and better reach marginalized MSM within current HIV prevention service provision. This study used national pharmaco‐epidemiology surveillance data and regional MSM population estimations to model the spatio‐temporal distribution of PrEP uptake among MSM in France 2016–2021 to identify marginalized MSM at risk for HIV and increase their PrEP uptake. Methods We first applied Bayesian spatial analyses with survey‐surveillance‐based HIV incidence data as a spatial proxy to estimate the size of (1) regional HIV‐negative MSM populations and (2) MSM who could be eligible for PrEP use according to French PrEP guidelines. We then applied Bayesian spatio‐temporal ecological regression modelling to estimate the regional prevalence and relative probability of the overall‐ and new‐PrEP uptake from 2016 to 2021 across France. Results HIV‐negative and PrEP‐eligible MSM populations vary regionally across France. Île‐de‐France was estimated to have the highest MSM density compared to other French regions. According to the final spatio‐temporal model, the relative probability of overall PrEP uptake was heterogeneous across France but remained stable over time. Urban areas have higher‐than‐average probabilities of PrEP uptake. The prevalence of PrEP use increased steadily (ranging from 8.8% [95% credible interval 8.5%;9.0%] in Nouvelle‐Aquitaine to 38.2% [36.5%;39.9%] in Centre‐Val‐de‐Loire in 2021). Conclusions Our results show that using Bayesian spatial analysis as a novel methodology to estimate the localized HIV‐negative MSM population is feasible and applicable. Spatio‐temporal models showed that despite the increasing prevalence of PrEP use in all regions, geographical disparities and inequalities of PrEP uptake continued to exist over time. We identified regions that would benefit from greater tailoring and delivery efforts. Based on our findings, public health policies and HIV prevention strategies could be adjusted to better combat HIV infections and to accelerate ending the HIV epidemic.

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Response to “On the risk of using raw regional data on new HIV infections in France” by Tassi et al.

Wang,

Molina,

Dray‐Spira

et al. 2024

J Int AIDS Soc.

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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

Cited by 6 publications

References 24 publications

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: A Bayesian ecological modelling analysis

Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: A Bayesian ecological modelling analysis

Spatio‐temporal changes in pre‐exposure prophylaxis uptake among MSM in mainland France between 2016 and 2021: a Bayesian small area approach with MSM population estimation

Response to “On the risk of using raw regional data on new HIV infections in France” by Tassi et al.

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