Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
CIHEB, University of Maryland School of Medicine, Baltimore, MD, United States of America.
PLoS One. 2022 Jun 8;17(6):e0268892. doi: 10.1371/journal.pone.0268892. eCollection 2022.
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.
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.
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.
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.
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.
尽管特定于地理位置的数据有助于针对艾滋病毒预防和治疗策略,但尼日利亚依赖国家和州级别的数据来制定政策和干预计划。我们计算了尼日利亚艾滋病毒护理连续体的州以下估计数。
利用 2018 年 7 月至 12 月尼日利亚艾滋病毒/艾滋病指标和影响调查(NAIIS)的数据,我们进行了一项地理空间分析,估计了三个关键的规划指标:成年人(15-64 岁)中艾滋病毒感染的流行率;艾滋病毒感染者中的抗逆转录病毒疗法(ART)覆盖率;艾滋病毒感染者中的病毒载量抑制率(VLS)。
我们使用一种名为堆叠概括的集成建模方法来分析可用的协变量,并使用地质统计学模型将堆叠的输出以及模型结果中的空间自相关纳入其中。为每个指标分别拟合模型。最后,我们在大约 5x5 公里的网格上生成每个指标的栅格估计值,并在州和地方政府区(LGA)级别生成估计值。
所有三个指标的估计值在州内和州间均有差异。虽然州级艾滋病毒流行率从 0.3%(95%置信区间[UI]:0.3%-0.5%])到 4.3%(95% UI:3.7%-4.9%)不等,但 LGA 的流行率从 0.2%(95% UI:0.1%-0.5%)到 8.5%(95% UI:5.8%-12.2%)不等。尽管州级和 LGA 级别的 ART 覆盖率范围没有实质性差异(25.6%-76.9%和 21.9%-81.9%),但州内 LGA 之间的 ART 覆盖率的平均绝对差异为 16.7 个百分点(范围为 3.5-38.5 个百分点)。ART 覆盖率在 LGA 之间差异较大的州,无论有效治疗覆盖率水平如何,VLS 的差异也很大-表明州级地理定位可能不足以解决覆盖差距。
艾滋病毒护理连续体的地理空间分析可以有效地突出州以下的差异,并确定需要进一步关注的领域,以实现疫情控制。通过生成地方估计值,政府、捐助者和其他实施伙伴将能够更好地进行有针对性的干预,并优先分配资源。