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dc.contributor.authorO'Brien-Carelli, Caitlin
dc.contributor.authorSteuben, Krista
dc.contributor.authorStafford, Kristen A
dc.contributor.authorAliogo, Rukevwe
dc.contributor.authorAlagi, Matthias
dc.contributor.authorJohanns, Casey K
dc.contributor.authorIbrahim, Jahun
dc.contributor.authorShiraishi, Ray
dc.contributor.authorEhoche, Akipu
dc.contributor.authorGreby, Stacie
dc.contributor.authorDirlikov, Emilio
dc.contributor.authorIbrahim, Dalhatu
dc.contributor.authorBronson, Megan
dc.contributor.authorAliyu, Gambo
dc.contributor.authorAliyu, Sani
dc.contributor.authorDwyer-Lindgren, Laura
dc.contributor.authorSwaminathan, Mahesh
dc.contributor.authorDuber, Herbert C
dc.contributor.authorCharurat, Man
dc.date.accessioned2022-06-10T11:56:21Z
dc.date.available2022-06-10T11:56:21Z
dc.date.issued2022-06-08
dc.identifier.urihttp://hdl.handle.net/10713/19106
dc.description.abstractObjective: Although geographically specific data can help target HIV prevention and treatment strategies, Nigeria relies on national- and state-level estimates for policymaking and intervention planning. We calculated sub-state estimates along the HIV continuum of care in Nigeria. Design: Using data from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) (July-December 2018), we conducted a geospatial analysis estimating three key programmatic indicators: prevalence of HIV infection among adults (aged 15-64 years); antiretroviral therapy (ART) coverage among adults living with HIV; and viral load suppression (VLS) rate among adults living with HIV. Methods: We used an ensemble modeling method called stacked generalization to analyze available covariates and a geostatistical model to incorporate the output from stacking as well as spatial autocorrelation in the modeled outcomes. Separate models were fitted for each indicator. Finally, we produced raster estimates of each indicator on an approximately 5×5-km grid and estimates at the sub-state/local government area (LGA) and state level. Results: Estimates for all three indicators varied both within and between states. While state-level HIV prevalence ranged from 0.3% (95% uncertainty interval [UI]: 0.3%-0.5%]) to 4.3% (95% UI: 3.7%-4.9%), LGA prevalence ranged from 0.2% (95% UI: 0.1%-0.5%) to 8.5% (95% UI: 5.8%-12.2%). Although the range in ART coverage did not substantially differ at state level (25.6%-76.9%) and LGA level (21.9%-81.9%), the mean absolute difference in ART coverage between LGAs within states was 16.7 percentage points (range, 3.5-38.5 percentage points). States with large differences in ART coverage between LGAs also showed large differences in VLS-regardless of level of effective treatment coverage-indicating that state-level geographic targeting may be insufficient to address coverage gaps. Conclusion: Geospatial analysis across the HIV continuum of care can effectively highlight sub-state variation and identify areas that require further attention in order to achieve epidemic control. By generating local estimates, governments, donors, and other implementing partners will be better positioned to conduct targeted interventions and prioritize resource distribution.en_US
dc.description.urihttps://doi.org/10.1371/journal.pone.0268892en_US
dc.language.isoenen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS ONEen_US
dc.titleMapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy.en_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0268892
dc.identifier.pmid35675346
dc.source.journaltitlePloS one
dc.source.volume17
dc.source.issue6
dc.source.beginpagee0268892
dc.source.endpage
dc.source.countryUnited States


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