为了评估肯尼亚的艾滋病毒流行,一系列类似设计的独立艾滋病毒指标家庭调查可用于调查与艾滋病毒预防和控制相关的关键指标的趋势,并描述地理和社会人口差异,评估干预措施的影响,并制定战略。我们开发了方法和工具,以促进对2007年,2012年和2018年在肯尼亚进行的三项全国家庭调查的趋势进行有力分析。
我们使用了2007年和2012年肯尼亚艾滋病指标调查(KAIS2007和KAIS2012)和2018年肯尼亚基于人口的艾滋病毒影响评估(KENPHIA2018)的数据。为了评估每个研究的设计和其他感兴趣的变量,变量被重新编码,以确保它们在三次调查中具有相同的含义。在评估权重程序的可比性之后,我们使用KAIS2012无应答加权程序来修正归一化的KENPHIA权重.分析仅限于所有三项调查所涵盖的地理区域。然后将修订的分析文件合并为单个文件用于合并分析。我们评估了年龄分布,性别,家庭财富,和城市/农村状况,以确定调查之间的意外变化。为了演示如何进行趋势分析,我们使用连续的,二进制,和时间到事件变量作为示例。具体来说,首次性行为年龄和过去12个月接受过HIV检测的时间趋势被用来证明所提出的分析方法.这些是用受访者特定的变量(年龄,性别,教育水平,和婚姻状况)和家庭变量(居住地和财富指数)。所有分析均在SAS9.4中进行,但以Stata和R格式创建分析文件以支持其他分析。
本研究展示了选定指标的趋势,以说明可在类似环境中使用的方法。早期性行为的发生率从2007年的每1000人年风险11.63(95%CI:10.95-12.34)下降到2012年的每1000人年风险10.45(95%CI:9.75-11.2)和2018年的每1000人年风险9.58(95%CI:9.08-10.1)。HIV检测率从2007年的12.6%(95%CI:11.6%-13.6%)上升至2012年的56.1%(95%CI:54.6%-57.6%),但在2018年略有下降至55.6%[95%CI:54.6%-56.6%)。在2007年至2012年之间可以令人信服地证明早期性行为发生率的下降,但在2012年至2018年之间却没有。同样,2012年和2018年的艾滋病毒检测率几乎没有差异。
我们的方法可用于支持低收入环境中HIV调查变量的趋势比较。可以评估独立的全国住户调查的可比性,适当调整,并用于估计关键指标的趋势。随着时间的推移,分析趋势不仅可以提供对肯尼亚艾滋病毒流行控制进展的见解,还可以找出差距。
For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018.
We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses.
This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95-12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75-11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08-10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%-13.6%) in 2007 to 56.1% (95% CI: 54.6%-57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%-56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018.
Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya\'s progress toward HIV epidemic control but also identify gaps.