J Acquir Immune Defic Syndr. 2021 Feb 24. doi: 10.1097/QAI.0000000000002675. Online ahead of print.
ABSTRACT
BACKGROUND: Pillar 4 of the United States’ End the HIV Epidemic plan is to respond quickly to HIV outbreaks, but the utility of CDC’s tool for identifying HIV outbreaks via time-space cluster detection has not been evaluated. The objective of this evaluation is to quantify the ability of the CDC time-space cluster criterion to predict future HIV diagnoses and to compare it to a space-time permutation statistic implemented in SaTScan software.
SETTING: Washington state from 2017 to 2019.
METHODS: We applied both cluster criteria to incident HIV cases in Washington State to identify clusters. Using a repeated measures Poisson model, we calculated a rate ratio comparing the 6-months following cluster detection to a baseline rate from 24 to 12 months before the cluster was detected. We also compared the demographics of cases within clusters to all other incident cases.
RESULTS: The CDC criteria identified 17 clusters containing 192 cases in the 6-months following cluster detection, corresponding to a rate ratio of 1.25 (95% CI 0.95-1.65) relative to baseline. The time-space permutation statistic identified 5 clusters containing 25 cases with a rate ratio of 2.27 (95% CI 1.28-4.03). Individuals in clusters identified by the new criteria were more likely to be of Hispanic origin (61% vs 20%) and in rural areas (51% vs 12%).
CONCLUSIONS: The space time permutation cluster analysis is a promising tool for identification of clusters with the largest growth potential for whom interruption may prove most beneficial.
PMID:33675622 | DOI:10.1097/QAI.0000000000002675
