Division of Epidemiology and Biostatistics, School of Public Health, University of Witwatersrand, Johannesburg, South Africa.
METRe Group, Department of International Health, Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, L3 5QA, UK.
BMC Public Health. 2020 Mar 23;20(1):379. doi: 10.1186/s12889-020-8436-z.
National or regional population-based HIV prevalence surveys have small sample sizes at district or sub-district levels; this leads to wide confidence intervals when estimating HIV prevalence at district level for programme monitoring and decision making. Health facility programme data, collected during service delivery is widely available, but since people self-select for HIV testing, HIV prevalence estimates based on it, is subject to selection bias. We present a statistical annealing technique, Hybrid Prevalence Estimation (HPE), that combines a small population-based survey sample with a facility-based sample to generate district level HIV prevalence estimates with associated confidence intervals.
We apply the HPE methodology to combine the 2011 Uganda AIDS indicator survey with the 2011 health facility HIV testing data to obtain HIV prevalence estimates for districts in Uganda. Multilevel logistic regression was used to obtain the propensity of testing for HIV in a health facility, and the propensity to test was used to combine the population survey and health facility HIV testing data to obtain the HPEs. We assessed comparability of the HPEs and survey-based estimates using Bland Altman analysis.
The estimates ranged from 0.012 to 0.178 and had narrower confidence intervals compared to survey-based estimates. The average difference between HPEs and population survey estimates was 0.00 (95% CI: - 0.04, 0.04). The HPE standard errors were 28.9% (95% CI: 23.4-34.4) reduced, compared to survey-based standard errors. Overall reduction in HPE standard errors compared survey-based standard errors ranged from 5.4 to 95%.
Facility data can be combined with population survey data to obtain more accurate HIV prevalence estimates for geographical areas with small population survey sample sizes. We recommend use of the methodology by district level managers to obtain more accurate HIV prevalence estimates to guide decision making without incurring additional data collection costs.
国家或地区性的基于人群的 HIV 流行率调查在区或分区级别上的样本量较小；这导致在为规划监测和决策制定而在区一级估计 HIV 流行率时，置信区间较宽。在提供服务期间收集的卫生机构规划数据广泛可用，但由于人们自行选择进行 HIV 检测，因此基于该数据的 HIV 流行率估计值存在选择偏倚。我们提出了一种统计退火技术，即混合流行率估计（HPE），该技术将小的基于人群的调查样本与基于机构的样本相结合，生成与置信区间相关的区一级 HIV 流行率估计值。
我们应用 HPE 方法将 2011 年乌干达艾滋病指标调查与 2011 年卫生机构 HIV 检测数据相结合，以获取乌干达各区的 HIV 流行率估计值。使用多水平逻辑回归获得在卫生机构中进行 HIV 检测的倾向，并用该倾向将人群调查和卫生机构 HIV 检测数据相结合，以获得 HPE。我们使用 Bland Altman 分析评估 HPE 与基于调查的估计值的可比性。
估计值范围从 0.012 到 0.178，置信区间比基于调查的估计值窄。HPE 与人群调查估计值之间的平均差异为 0.00（95%CI：-0.04，0.04）。与基于调查的标准误差相比，HPE 的标准误差降低了 28.9%（95%CI：23.4-34.4）。总体而言，与基于调查的标准误差相比，HPE 标准误差的降幅为 5.4%至 95%。
可以将机构数据与人群调查数据相结合，以获取样本量较小的地理区域更准确的 HIV 流行率估计值。我们建议由地区级管理人员使用该方法获得更准确的 HIV 流行率估计值，以指导决策制定，而无需额外增加数据收集成本。
