Open AccessEstimating Hidden Population Sizes with Venue-based Sampling
Author(s) -
Ashton M. Verdery,
Sharon S. Weir,
Zahra Reynolds,
Grace E. Mulholland,
Jessie K. Edwards
Publication year - 2019
Publication title -
epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.901
H-Index - 173
eISSN - 1531-5487
pISSN - 1044-3983
DOI - 10.1097/ede.0000000000001059
Subject(s) - population , sample size determination , sample (material) , inference , sampling (signal processing) , scale (ratio) , computer science , key (lock) , population size , estimation , statistics , artificial intelligence , medicine , geography , mathematics , environmental health , computer security , cartography , engineering , chemistry , filter (signal processing) , chromatography , systems engineering , computer vision
Researchers use a variety of population size estimation methods to determine the sizes of key populations at elevated risk of human immunodeficiency virus (HIV)/acquired immune deficiency syndrome (AIDS), an important step in quantifying epidemic impact, advocating for high-risk groups, and planning, implementing, and monitoring prevention, care, and treatment programs. Conventional procedures often use information about sample respondents' social network contacts to estimate the sizes of key populations of interest. A recent study proposes a generalized network scale-up method that combines two samples-a traditional sample of the general population and a link-tracing sample of the hidden population-and produces more accurate results with fewer assumptions than conventional approaches.