TY - JOUR
T1 - Mapping HIV prevalence in Nigeria using small area estimates to develop a targeted HIV intervention strategy
AU - O'Brien-Carelli, Caitlin
AU - Steuben, Krista
AU - Stafford, Kristen A.
AU - Aliogo, Rukevwe
AU - Alagi, Matthias
AU - Johanns, Casey K.
AU - Ibrahim, Jahun
AU - Shiraishi, Ray
AU - Ehoche, Akipu
AU - Greby, Stacie
AU - Dirlikov, Emilio
AU - Ibrahim, Dalhatu
AU - Bronson, Megan
AU - Aliyu, Gambo
AU - Aliyu, Sani
AU - Dwyer-Lindgren, Laura
AU - Swaminathan, Mahesh
AU - Duber, Herbert C.
AU - Charurat, Man
N1 - Publisher Copyright:
Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PY - 2022/6
Y1 - 2022/6
N2 - Objective 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.
AB - Objective 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.
UR - http://www.scopus.com/inward/record.url?scp=85131772419&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0268892
DO - 10.1371/journal.pone.0268892
M3 - Article
C2 - 35675346
AN - SCOPUS:85131772419
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 6 June
M1 - e0268892
ER -