Estimating HIV Incidence Using a Cross-Sectional Survey: Comparison of Three Approaches in a Hyperendemic Setting, Ndhiwa Subcounty, Kenya, 2012.
Estimating HIV incidence is critical for identifying groups at risk for HIV infection, planning and targeting interventions, and evaluating these interventions over time. The use of reliable estimation methods for HIV incidence is thus of high importance. The aim of this study was to compare methods for estimating HIV incidence in a population-based cross-sectional survey.



The incidence estimation methods evaluated included assay-derived methods, a testing history-derived method, and a probability-based method applied to data from the Ndhiwa HIV Impact in Population Survey (NHIPS). Incidence rates by sex and age and cumulative incidence as a function of age were presented.



HIV incidence ranged from 1.38 [95% confidence interval (CI) 0.67-2.09] to 3.30 [95% CI 2.78-3.82] per 100 person-years overall; 0.59 [95% CI 0.00-1.34] to 2.89 [95% CI 0.86-6.45] in men; and 1.62 [95% CI 0.16-6.04] to 4.03 [95% CI 3.30-4.77] per 100 person-years in women. Women had higher incidence rates than men for all methods. Incidence rates were highest among women aged 15-24 and 25-34 years and highest among men aged 25-34 years.



Comparison of different methods showed variations in incidence estimates, but they were in agreement to identify most-at-risk groups. The use and comparison of several distinct approaches for estimating incidence are important to provide the best-supported estimate of HIV incidence in the population.
1 Hospices Civils de Lyon, Service de Biostatistique , Lyon, France .|2 Université de Lyon , Lyon, France .|3 Université Lyon 1 , Villeurbanne, France .|4 CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé , Villeurbanne, France .
Stéphanie
S
Blaizot
5 Division of Global HIV/AIDS, U.S. Centers for Disease Control and Prevention , Nairobi, Kenya .
Andrea A
AA
Kim
6 Division of HIV/AIDS Prevention, U.S. Centers for Disease Control and Prevention , Kisumu, Kenya .
Clement
C
Zeh
1 Hospices Civils de Lyon, Service de Biostatistique , Lyon, France .|2 Université de Lyon , Lyon, France .|3 Université Lyon 1 , Villeurbanne, France .|4 CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé , Villeurbanne, France .
Benjamin
B
Riche
7 Epicentre , Paris, France .
David
D
Maman
5 Division of Global HIV/AIDS, U.S. Centers for Disease Control and Prevention , Nairobi, Kenya .
Kevin M
KM
De Cock
7 Epicentre , Paris, France .|8 UMI 233 TransVIHMI, Institut de Recherche pour le Développement, INSERM U1175, Université de Montpellier , Montpellier, France .
Jean-François
JF
Etard
1 Hospices Civils de Lyon, Service de Biostatistique , Lyon, France .|2 Université de Lyon , Lyon, France .|3 Université Lyon 1 , Villeurbanne, France .|4 CNRS UMR 5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique-Santé , Villeurbanne, France .
René
R
Ecochard
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D003430:Cross-Sectional Studies; D004812:Epidemiologic Methods; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D007630:Kenya / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D012737:Sex Factors; D055815:Young Adult
Africa; HIV/AIDS epidemic; incidence; incidence assay; incidence estimation; statistical model