Development of methods for cross-sectional HIV incidence estimation in a large, community randomized trial.
BACKGROUND
Accurate methods of HIV incidence determination are critically needed to monitor the epidemic and determine the population level impact of prevention trials. One such trial, Project Accept, a Phase III, community-randomized trial, evaluated the impact of enhanced, community-based voluntary counseling and testing on population-level HIV incidence. The primary endpoint of the trial was based on a single, cross-sectional, post-intervention HIV incidence assessment.


RESULTS
Test performance of HIV incidence determination was evaluated for 403 multi-assay algorithms [MAAs] that included the BED capture immunoassay [BED-CEIA] alone, an avidity assay alone, and combinations of these assays at different cutoff values with and without CD4 and viral load testing on samples from seven African cohorts (5,325 samples from 3,436 individuals with known duration of HIV infection [1 month to >10 years]). The mean window period (average time individuals appear positive for a given algorithm) and performance in estimating an incidence estimate (in terms of bias and variance) of these MAAs were evaluated in three simulated epidemic scenarios (stable, emerging and waning). The power of different test methods to detect a 35% reduction in incidence in the matched communities of Project Accept was also assessed. A MAA was identified that included BED-CEIA, the avidity assay, CD4 cell count, and viral load that had a window period of 259 days, accurately estimated HIV incidence in all three epidemic settings and provided sufficient power to detect an intervention effect in Project Accept.


CONCLUSIONS
In a Southern African setting, HIV incidence estimates and intervention effects can be accurately estimated from cross-sectional surveys using a MAA. The improved accuracy in cross-sectional incidence testing that a MAA provides is a powerful tool for HIV surveillance and program evaluation.
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America ; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
Oliver
O
Laeyendecker
Michal
M
Kulich
Deborah
D
Donnell
Arnošt
A
Komárek
Marek
M
Omelka
Caroline E
CE
Mullis
Greg
G
Szekeres
Estelle
E
Piwowar-Manning
Agnes
A
Fiamma
Ronald H
RH
Gray
Tom
T
Lutalo
Charles S
CS
Morrison
Robert A
RA
Salata
Tsungai
T
Chipato
Connie
C
Celum
Erin M
EM
Kahle
Taha E
TE
Taha
Newton I
NI
Kumwenda
Quarraisha Abdool
QA
Karim
Vivek
V
Naranbhai
Jairam R
JR
Lingappa
Michael D
MD
Sweat
Thomas
T
Coates
Susan H
SH
Eshleman
D000349:Africa / Q000453:epidemiology; D000465:Algorithms; D003430:Cross-Sectional Studies / Q000379:methods; D058872:Epidemics; D005260:Female; D015658:HIV Infections / Q000453:epidemiology*; D006801:Humans; D015994:Incidence; D008297:Male; D016032:Randomized Controlled Trials as Topic