Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya

Waruru, Anthony; Achia, Thomas; Tobias, James L.; Ng'ang'a, James; Mwangi, M.; Wamicwe, Joyce; Zielinski-Gutiérrez, Emily; Oluoch, Tom; Muthama, E; Tylleskär, Thorkild

doi:10.1097/qai.0000000000001652

Cited by 15 publications

(12 citation statements)

References 31 publications

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“…In Siaya, clusters of higher new HIV diagnoses were found in areas around specific towns, around major roads, near a major road intersection and adjacent to a beach. Although geospatial clustering of new HIV diagnoses has not yet been described in the literature, other studies have described the clustering of higher HIV prevalence [ 10 , 13 ] and incidence [ 12 ] around similar ecological factors. The clustering around ecological features observed in our study suggests that population-level factors related to the ecological features, including socioeconomic, mobility and geographic factors, may influence the clustering of new HIV diagnoses.…”

Section: Discussionmentioning

confidence: 99%

Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

et al. 2021

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Background As countries make progress towards HIV epidemic control, there is increasing need to identify finer geographic areas to target HIV interventions. We mapped geographic clusters of new HIV diagnoses, and described factors associated with HIV-positive diagnosis, in order to inform targeting of HIV interventions to finer geographic areas and sub-populations. Methods We analyzed data for clients aged > 15 years who received home-based HIV testing as part of a routine public health program between May 2016 and July 2017 in Siaya County, western Kenya. Geospatial analysis using Kulldorff’s spatial scan statistic was used to detect geographic clusters (radius < 5 kilometers) of new HIV diagnoses. Factors associated with new HIV diagnosis were assessed in a spatially-integrated Bayesian hierarchical model. Results Of 268,153 clients with HIV test results, 2906 (1.1%) were diagnosed HIV-positive. We found spatial variation in the distribution of new HIV diagnoses, and identified nine clusters in which the number of new HIV diagnoses was significantly (1.56 to 2.64 times) higher than expected. Sub-populations with significantly higher HIV-positive yield identified in the multivariable spatially-integrated Bayesian model included: clients aged 20–24 years [adjusted relative risk (aRR) 3.45, 95% Bayesian Credible Intervals (CI) 2.85–4.20], 25–35 years (aRR 4.76, 95% CI 3.92–5.81) and > 35 years (aRR 2.44, 95% CI 1.99–3.00); those in polygamous marriage (aRR 1.84, 95% CI 1.55–2.16), or separated/divorced (aRR 3.36, 95% CI 2.72–4.08); and clients who reported having never been tested for HIV (aRR 2.35, 95% CI 2.02–2.72), or having been tested > 12 months ago (aRR 1.53, 95% CI 1.41–1.66). Conclusion Our study used routine public health program data to identify granular geographic clusters of higher new HIV diagnoses, and sub-populations with higher HIV-positive yield in the setting of a generalized HIV epidemic. In order to target HIV testing and prevention interventions to finer granular geographic areas for maximal epidemiologic impact, integrating geospatial analysis into routine public health programs can be useful.

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Section: Discussionmentioning

confidence: 99%

Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

et al. 2021

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“…Understanding individuallevel characteristics that could be associated with HIV testingseeking behavior is equally important. For example, using a household level survey, Waruru et al found that economic status, perception of HIV risk, mobility, transactional sex, and uncircumcised men were associated with living in high HIV prevalence clusters in Kenya (8).…”

Section: Precision Targetingmentioning

confidence: 99%

“…Previous analysis of population-based household survey data in Kenya has shown that clusters of high rates of HIV infections may exist even in low burden counties (8). Examining the HIV burden without regard to arbitrary physical boundaries that may confine populations by ethnicity, culture, human settlements, and natural resources provides an opportunity for equitable resource allocation.…”

Section: Introductionmentioning

confidence: 99%

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Waruru

Wamicwe

Mwangi

et al. 2021

Front. Public Health

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Background: The UNAIDS 90-90-90 Fast-Track targets provide a framework for assessing coverage of HIV testing services (HTS) and awareness of HIV status – the “first 90.” In Kenya, the bulk of HIV testing targets are aligned to the five highest HIV-burden counties. However, we do not know if most of the new HIV diagnoses are in these five highest-burden counties or elsewhere.Methods: We analyzed facility-level HTS data in Kenya from 1 October 2015 to 30 September 2016 to assess the spatial distribution of newly diagnosed HIV-positives. We used the Moran's Index (Moran's I) to assess global and local spatial auto-correlation of newly diagnosed HIV-positive tests and Kulldorff spatial scan statistics to detect hotspots of newly diagnosed HIV-positive tests. For aggregated data, we used Kruskal-Wallis equality-of-populations non-parametric rank test to compare absolute numbers across classes.Results: Out of 4,021 HTS sites, 3,969 (98.7%) had geocodes available. Most facilities (3,034, 76.4%), were not spatially autocorrelated for the number of newly diagnosed HIV-positives. For the rest, clustering occurred as follows; 438 (11.0%) were HH, 66 (1.7%) HL, 275 (6.9%) LH, and 156 (3.9%) LL. Of the HH sites, 301 (68.7%) were in high HIV-burden counties. Over half of 123 clusters with a significantly high number of newly diagnosed HIV-infected persons, 73(59.3%) were not in the five highest HIV-burden counties. Clusters with a high number of newly diagnosed persons had twice the number of positives per 1,000,000 tests than clusters with lower numbers (29,856 vs. 14,172).Conclusions: Although high HIV-burden counties contain clusters of sites with a high number of newly diagnosed HIV-infected persons, we detected many such clusters in low-burden counties as well. To expand HTS where most needed and reach the “first 90” targets, geospatial analyses and mapping make it easier to identify and describe localized epidemic patterns in a spatially dispersed epidemic like Kenya's, and consequently, reorient and prioritize HTS strategies.

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“…Faria et al used the HIV-1 sequence data from Central Africa and reconstructed the early stage of HIV-1 transmission history; they emphasized that both social changes and transport networks played important roles in the viral establishment in human populations [39]. The geospatial viral migration patterns and temporal dynamics of HIV-1 transmission can be further reconstructed when molecular network analysis is combined with both geographical and temporal information [35,40,41]. A study on the HIV-1 epidemic in the Nordic countries found both different HIV-1 transmission patterns between countries and linkages in a large geographical region; Denmark and Sweden showed the strongest geographical link, and Denmark had a great part of heterosexual domestic spread of HIV-1 subtype B [32].…”

Section: Molecular Network Reconstruct the History Of Hiv Spreadmentioning

confidence: 99%

Molecular network-based intervention brings us closer to ending the HIV pandemic

Han

Zhao

et al. 2020

Front. Med.

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Precise identification of HIV transmission among populations is a key step in public health responses. However, the HIV transmission network is usually difficult to determine. HIV molecular networks can be determined by phylogenetic approach, genetic distance-based approach, and a combination of both approaches. These approaches are increasingly used to identify transmission networks among populations, reconstruct the history of HIV spread, monitor the dynamics of HIV transmission, guide targeted intervention on key subpopulations, and assess the effects of interventions. Simulation and retrospective studies have demonstrated that these molecular network-based interventions are more cost-effective than random or traditional interventions. However, we still need to address several challenges to improve the practice of molecular network-guided targeting interventions to finally end the HIV epidemic. The data remain limited or difficult to obtain, and more automatic real-time tools are required. In addition, molecular and social networks must be combined, and technical parameters and ethnic issues warrant further studies.

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Finding Hidden HIV Clusters to Support Geographic-Oriented HIV Interventions in Kenya

Cited by 15 publications

References 31 publications

Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

Mapping geographic clusters of new HIV diagnoses to inform granular-level interventions for HIV epidemic control in western Kenya

Where Are the Newly Diagnosed HIV Positives in Kenya? Time to Consider Geo-Spatially Guided Targeting at a Finer Scale to Reach the “First 90”

Molecular network-based intervention brings us closer to ending the HIV pandemic

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