Should adjustment for covariates be used in prevalence estimations?

Citation data:

Epidemiologic perspectives & innovations : EP+I, ISSN: 1742-5573, Vol: 5, Issue: 1, Page: 2

Publication Year:
2008
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Repository URL:
https://works.bepress.com/elizabeth_bertone-johnson/3; https://works.bepress.com/wenjun_li/49; https://works.bepress.com/edward_stanek/4; https://escholarship.umassmed.edu/oapubs/1969
PMID:
18221545
DOI:
10.1186/1742-5573-5-2
PMCID:
PMC2254620
Author(s):
Li, Wenjun; Stanek, Edward J.; Bertone-Johnson, Elizabeth R.
Publisher(s):
Springer Nature
Tags:
Medicine; Life Sciences; Medicine and Health Sciences
article description
Adjustment for covariates (also called auxiliary variables in survey sampling literature) is commonly applied in health surveys to reduce the variances of the prevalence estimators. In theory, adjusted prevalence estimators are more accurate when variance components are known. In practice, variance components needed to achieve the adjustment are unknown and their sample estimators are used instead. The uncertainty introduced by estimating variance components may overshadow the reduction in the variance of the prevalence estimators due to adjustment. We present empirical guidelines indicating when adjusted prevalence estimators should be considered, using gender adjusted and unadjusted smoking prevalence as an illustration.