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A New General Biomarker-based Incidence Estimator

Kassanjee, Reshmaa,b; McWalter, Thomas A.a,b; Bärnighausen, Tillc,d; Welte, Alexa

doi: 10.1097/EDE.0b013e3182576c07

Background: Estimating disease incidence from cross-sectional surveys, using biomarkers for “recent” infection, has attracted much interest. Despite widespread applications to HIV, there is currently no consensus on the correct handling of biomarker results classifying persons as “recently” infected long after the infections occurred.

Methods: We derive a general expression for a weighted average of recent incidence that—unlike previous estimators—requires no particular assumption about recent infection biomarker dynamics or about the demographic and epidemiologic context. This is possible through the introduction of an explicit timescale T that truncates the period of averaging implied by the estimator.

Results: The recent infection test dynamics can be summarized into 2 parameters, similar to those appearing in previous estimators: a mean duration of recent infection and a false-recent rate. We identify a number of dimensionless parameters that capture the bias that arises from working with tractable forms of the resulting estimator and elucidate the utility of the incidence estimator in terms of the performance of the recency test and the population state. Estimation of test characteristics and incidence is demonstrated using simulated data. The observed confidence interval coverage of the test characteristics and incidence is within 1% of intended coverage.

Conclusions: Biomarker-based incidence estimation can be consistently adapted to a general context without the strong assumptions of previous work about biomarker dynamics and epidemiologic and demographic history.

Supplemental Digital Content is available in the text.

From the aDST/NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa; bSchool of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa; cDepartment of Global Health and Population, Harvard School of Public Health, Boston, MA; and dAfrica Centre for Health and Population Studies, University of KwaZulu-Natal, Mtubatuba, South Africa.

Submitted 2 August 2011; accepted 2 March 2012; posted 23 May 2012.

Supported in part by a grant from the Canadian International Development Agency; and by the National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), grant 1R01-HD058482-01 (to T.B.). The authors reported no other financial interests related to this research.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article ( This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Reshma Kassanjee, SACEMA, c/o STIAS, Private Bag X1, Matieland, 7602, Stellenbosch, South Africa. Email:

© 2012 Lippincott Williams & Wilkins, Inc.