Centre for International Health, University of Bergen, Bergen, Norway.
Division of Global HIV and TB, U.S Centers for Disease Control and Prevention, Nairobi, Kenya.
Front Public Health. 2021 Apr 23;9:503555. doi: 10.3389/fpubh.2021.503555. eCollection 2021.
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. 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. 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). 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.
UNAIDS 90-90-90 加速目标为评估艾滋病毒检测服务（HTS）的覆盖范围和艾滋病毒感染状况的知晓率（“前 90”）提供了一个框架。在肯尼亚，大部分艾滋病毒检测目标与艾滋病毒负担最重的五个县相一致。然而，我们不知道大多数新诊断的艾滋病毒感染者是否在这五个负担最重的县或其他地方。我们分析了 2015 年 10 月 1 日至 2016 年 9 月 30 日肯尼亚的医疗机构水平 HTS 数据，以评估新诊断出的 HIV 阳性病例的空间分布情况。我们使用 Moran 指数（Moran's I）评估新诊断出的 HIV 阳性检测的全球和局部空间自相关，使用 Kulldorff 空间扫描统计来检测新诊断出的 HIV 阳性检测的热点。对于聚合数据，我们使用 Kruskal-Wallis 群体均等性非参数秩检验来比较类别之间的绝对数量。在 4021 个 HTS 点中，有 3969 个（98.7%）有地理位置编码。大多数机构（3034 个，76.4%）的新诊断出的 HIV 阳性病例数量没有空间自相关。其余的病例如下；HH 有 438 个（11.0%），HL 有 66 个（1.7%），LH 有 275 个（6.9%），LL 有 156 个（3.9%）。在 HH 点中，有 301 个（68.7%）位于艾滋病毒负担沉重的县。在 123 个新诊断出 HIV 感染者人数显著较高的集群中，有 73 个（59.3%）不在五个艾滋病毒负担最重的县。高数量新诊断感染者的集群每 100 万次检测的阳性人数是低数量集群的两倍（29856 对 14172）。虽然艾滋病毒负担沉重的县包含大量新诊断出的 HIV 感染者人数较多的集群，但我们在低负担县也发现了许多这样的集群。为了在最需要的地方扩大 HTS 服务，并实现“前 90”目标，地理空间分析和制图使我们更容易识别和描述像肯尼亚这样的空间分散性流行中的局部疫情模式，从而调整和优先考虑 HTS 战略。
