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Conceived and designed the experiments: TB AP MLN. Performed the experiments: JV NG AP. Analyzed the data: TB AW TAM FT MLN. Contributed reagents/materials/analysis tools: CW AW TAM NM JV NG FT AP. Wrote the paper: TB. Organized data collection: CW. Contributed to data collection: NM.

The BED IgG-Capture Enzyme Immunoassay (cBED assay), a test of recent HIV infection, has been used to estimate HIV incidence in cross-sectional HIV surveys. However, there has been concern that the assay overestimates HIV incidence to an unknown extent because it falsely classifies some individuals with non-recent HIV infections as recently infected. We used data from a longitudinal HIV surveillance in rural South Africa to measure the fraction of people with non-recent HIV infection who are falsely classified as recently HIV-infected by the cBED assay (the long-term false-positive ratio (FPR)) and compared cBED assay-based HIV incidence estimates to longitudinally measured HIV incidence.

We measured the long-term FPR in individuals with two positive HIV tests (in the HIV surveillance, 2003–2006) more than 306 days apart (sample size ^{th} percentile of incidence values. We observed 4,869 individuals over 7,685 person-years for longitudinal HIV incidence estimation. The long-term FPR was 0.0169 (95% CI 0.0100–0.0266). Using this FPR, the cross-sectional cBED-based HIV incidence estimates (per 100 people per year) varied between 3.03 (95% CI 2.44–3.63) and 3.19 (95% CI 2.57–3.82), depending on the incidence formula. Using a long-term FPR of 0.0560 based on previous studies, HIV incidence estimates varied between 0.65 (95% CI 0.00–1.32) and 0.71 (95% CI 0.00–1.43). The longitudinally measured HIV incidence was 3.09 per 100 people per year (95% CI 2.69–3.52), after adjustment to the sex-age distribution of the sample used in cBED assay-based estimation.

In a rural community in South Africa with high HIV prevalence, the long-term FPR of the cBED assay is substantially lower than previous estimates. The cBED assay performs well in HIV incidence estimation if the locally measured long-term FPR is used, but significantly underestimates incidence when a FPR estimate based on previous studies in other settings is used.

To understand the dynamics of the HIV epidemic and to target and evaluate interventions to prevent HIV infection, estimates of HIV incidence at the population level are of prime importance. HIV incidence estimates can be obtained through repeated HIV testing of individuals in longitudinal surveillances. Such surveillances, however, are difficult to establish and expensive to maintain. Longitudinal data on HIV status are thus rarely available

In recent years, a number of large-scale cross-sectional HIV serosurveys have been conducted. For instance, between 2001 and 2008, 20 demographic health surveys (DHS) in developing countries have included nationally representative HIV serosurveys

The cBED assay has been used to estimate HIV incidence in many countries, including in Ethiopia

We use data from a large population-based longitudinal HIV surveillance to measure the long-term FPR in a rural African community with high HIV prevalence

We used dried blood spot (DBS) specimens which were collected in the longitudinal population-based HIV surveillance conducted by the Africa Centre for Health and Population Studies (Africa Centre), University of KwaZulu-Natal

All women aged 15–49 years and all men aged 15–54 years who were resident in the surveillance area at the time of visit of an HIV surveillance fieldworker were eligible for HIV testing. Different samples were used for the different analyses conducted for this article. The samples for estimation of the long-term FPR consisted of cBED assay results for blood specimens contributed by individuals who tested HIV positive in the surveillance in the time period from June 2003 through June 2006. In order to be included in the sample, the specimens had to meet the following criteria. First, they were follow-up specimens from individuals who had previously tested HIV-positive in the surveillance. Second, the time period between the first positive HIV test and the follow-up specimen exceeded the maximum BED progression time. Third, the specimen was the earliest follow-up specimen that met the second criterion. Our count of long-term false-positive individuals included all individuals who were classified as recently HIV-infected and had been infected for longer than the maximum BED progression time, i.e. it included both non-progressors and regressors.

For the further cBED assay analyses we used a maximum BED progression time of 306 days (sample size

Maximum BED progression time | Sample size | Number of individuals with false-positive cBED assay results | Long-term FPR (ε_{2}) | |

(in days) | (individuals) | (individuals) | Mean | 95% CI |

250 | 1100 | 18 | 0.0164 | 0.0097–0.0257 |

260 | 1094 | 18 | 0.0165 | 0.0098–0.0259 |

270 | 1090 | 18 | 0.0165 | 0.0098–0.0260 |

280 | 1083 | 18 | 0.0166 | 0.0099–0.0261 |

290 | 1081 | 18 | 0.0167 | 0.0099–0.0262 |

300 | 1070 | 18 | 0.0168 | 0.0100–0.0265 |

18 | ||||

310 | 1056 | 18 | 0.0170 | 0.0101–0.0268 |

320 | 1043 | 18 | 0.0173 | 0.0103–0.0271 |

330 | 1035 | 18 | 0.0174 | 0.0103–0.0273 |

340 | 1017 | 18 | 0.0177 | 0.0105–0.0278 |

350 | 991 | 17 | 0.0172 | 0.0100–0.0273 |

370 | 818 | 14 | 0.0171 | 0.0094–0.0285 |

380 | 773 | 14 | 0.0181 | 0.0099–0.0302 |

390 | 755 | 14 | 0.0185 | 0.0102–0.0309 |

400 | 737 | 14 | 0.0190 | 0.0104–0.0317 |

FPR = false-positive ratio, CI = confidence interval. Row in bold font shows FPR at twice the window period of 153, 180, and 187 days, respectively.

For the HIV incidence estimation based on longitudinal HIV status information, we included all individuals who tested at least twice for HIV in the period from June 2003 through June 2006 and whose first HIV test in this period was negative (4,869 individuals observed over 7,685 person-years). As in previous studies of HIV incidence based on data from longitudinal HIV surveillances

For the cross-sectional cBED-based HIV incidence estimation, we used the first available HIV test for all individuals tested in the time period January 2005 through June 2006 (

HIV status was determined by antibody testing with a broad-based HIV-1/HIV-2 enzyme-linked immunosorbent assay (ELISA; Vironostika, Organon Teknika, Boxtel, the Netherlands) followed by a confirmatory ELISA (GAC-ELISA; Abbott, Abbott Park, Illinois, USA)

Different formulae that use information obtained from the cBED assay have been proposed to estimate HIV incidence from cross-sectional surveys. These formulae provide incidence estimates expressed either as a rate, _{r}_{p}_{1} is the short-term FPR (i.e. over the period [_{2} is the long-term FPR (i.e. over all times _{1} and _{2}, are related to the FPRs by _{1} = 1−_{1} and _{2} = 1−_{2}, respectively. The formula of Hargrove and colleagues (Hargrove formula)

In addition, we implemented a simplified version of the McDougal formula. The adjustment factor used in the formula can be simplified to

Note that in order to implement any of the above four formulae, estimates of the long-term FPR _{2} and the window period

Note also that the Hargrove, McWalter/Welte and simplified McDougal formulae do not require estimates of _{1}, which – unlike _{2} – cannot be calibrated from longitudinal data if the intervals between the last negative and the first positive HIV test in seroconverters are of the order of one year _{1} (0.2770) from another study in order to implement the McDougal formula

The McWalter/Welte formula expresses HIV incidence as a rate, i.e. as the number of HIV seroconversions per person-time at risk, while all other formulae express HIV incidence as an incidence proportion, i.e. the number of HIV seroconversions within a specified time period divided by the size of the population initially at risk. In order to directly compare all HIV incidence estimates in our study, we expressed the estimates based on the McWalter/Welte formula and the longitudinally measured HIV incidence both as rates (per 100 person-years) and as incidence proportions (per 100 people per year). We translated the rate estimates into proportions, assuming that the incidence rate, _{r}

The authors of the four different formulae do not use equivalent methods for the calculation of confidence intervals (CIs). Thus, uncertainty analysis on the incidence estimates was performed as follows. Any observed proportion of HIV-negative, cBED-recent and cBED-non-recent individuals is an unbiased estimate of the underlying population proportions. Given an observed occurrence of the population proportions and the sample size, all attainable draws of the three counts can be enumerated and assigned their respective trinomial probability. Hence an exact cumulative probability distribution of attainable values of the incidence estimator can be computed. For each incidence estimate, we quote the estimator evaluated at the observed counts (the maximum likelihood estimate) and a confidence interval expressed as the central 95^{th} percentile.

To control for differences in the sex-age composition between the sample used in the longitudinal HIV incidence estimation and the sample used in the cBED assay-based estimation, we weighted the sex- and five-year age group-specific longitudinal mean incidence rates by the proportions of individuals in each of the sex-age groups in the sample used for the cBED assay-based estimation_{rs}_{si}_{ri}_{rs}_{rs}_{i}_{rs}

Counting the number of DBS specimens classified as recently HIV-infected by the cBED assay in the sample of all individuals who had a previous positive HIV test more than 306 days before the date of the cBED assay-tested specimen, we obtained a long-term FPR of 0.0169 (95% CI 0.0100–0.0266). When we varied the length of the maximum BED progression time from 250 to 400 days (in daily intervals), we found that the estimate of the long-term FPR did not change significantly over the time interval, with minimum and maximum long-term FPRs of 0.0164 (95% CI 0.0097–0.0257) and 0.0190 (95% CI 0.0104–0.0317), respectively (

Of the 4,869 individuals included in the sample for longitudinal HIV incidence measurement, 224 people seroconverted in 7,685 person-years. Assuming that seroconversion occurred at the mid-date between the last available negative HIV test and the first available positive HIV test, longitudinally measured crude HIV incidence was 2.87 per 100 people per year (95% CI 2.53–3.27) (

Estimation type | Unit | HIV incidence | |

Mean | 95% CI | ||

(7,685 person-years, 224 seroconversions) | |||

Crude | (per 100 person-years) | 2.91 | 2.56–3.32 |

Sex-age adjusted | (per 100 person-years) | 3.14 | 2.73–3.58 |

Crude | (per 100 people per year) | 2.87 | 2.53–3.27 |

Sex-age adjusted | (per 100 people per year) | 3.09 | 2.69–3.52 |

( | |||

_{2} = 0.0169) | |||

McWalter/Welte | (per 100 person-years) | 3.22 | 2.57–3.87 |

McWalter/Welte | (per 100 people per year) | 3.17 | 2.54–3.80 |

McDougal | (per 100 people per year) | 3.03 | 2.44–3.63 |

Hargrove | (per 100 people per year) | 3.19 | 2.57–3.82 |

McDougal, simplified | (per 100 people per year) | 3.12 | 2.51–3.73 |

_{2} = 0.0100) | |||

McWalter/Welte | (100 person-years) | 3.65 | 3.00–4.32 |

McWalter/Welte | (per 100 people per year) | 3.58 | 2.95–4.22 |

McDougal | (per 100 people per year) | 3.40 | 2.82–4.00 |

Hargrove | (per 100 people per year) | 3.57 | 2.95–4.19 |

McDougal, simplified | (per 100 people per year) | 3.52 | 2.91–4.14 |

_{2} = 0.0266) | |||

McWalter/Welte | (100 person-years) | 2.60 | 1.96–3.27 |

McWalter/Welte | (per 100 people per year) | 2.57 | 1.94–3.22 |

McDougal | (per 100 people per year) | 2.49 | 1.89–3.11 |

Hargrove | (per 100 people per year) | 2.63 | 1.99–3.29 |

McDougal, simplified | (per 100 people per year) | 2.53 | 1.92–3.17 |

_{2} = 0.0560) | |||

McWalter/Welte | (100 person-years) | 0.65 | 0.00–1.33 |

McWalter/Welte | (per 100 people per year) | 0.65 | 0.00–1.32 |

McDougal | (per 100 people per year) | 0.66 | 0.00–1.33 |

Hargrove | (per 100 people per year) | 0.71 | 0.00–1.43 |

McDougal, simplified | (per 100 people per year) | 0.65 | 0.00–1.32 |

CI = confidence interval, FPR = false-positive ratio.

Of the 11,755 individuals included in the sample for the cBED assay-based HIV incidence measurement, 9,236 tested HIV- negative and 2,519 HIV-positive. Of the individuals who tested HIV-positive, 165 were classified in cBED assay testing as recently HIV-infected and the remainder as non-recently infected. For given _{2} and _{2} of 0.0169, HIV incidence point estimates (per 100 people per year) varied between 3.03 (95% CI 2.44–3.63; McDougal formula) and 3.19 (95% CI 2.57–3.82; Hargrove formula) (

Our finding that the cBED assay-based HIV incidence estimate was not significantly different from the longitudinal HIV incidence estimate did not change when we applied the window periods of 180 and 187 days (and their corresponding long-term FPRs of 0.0182 and 0.0177 (see

As described above, we conducted sensitivity analysis of the longitudinally measured HIV incidence estimate by changing the assumption about seroconversion dates. Assuming that all seroconverters became HIV-seropositive on the day following the last negative HIV test, crude HIV incidence was estimated at 2.97 per 100 person-years (95% CI 2.61–3.39). Assuming, on the other hand, that all seroconverters became HIV-seropositive on the day of their first positive HIV test, crude HIV incidence was estimated at 2.85 per 100 person-years (95% CI 2.51–3.25). The longitudinal HIV incidence estimates were thus highly robust to changes in the approach to computing the seroconversion date. Even under the most extreme possible assumptions, the mean HIV incidence changed by only 2% of the estimate based on the mid-date assumption, as reported in

When we stratified HIV incidence by sex and five-year age group (starting at 15 years of age), we found that none of the cBED assay-based sex and age-specific estimates differed significantly from the corresponding longitudinally measured sex and age-specific estimates. However, our samples in each of the sex-age groups were too small to detect significant differences with reasonable confidence. The coefficients of variation (CVs) of the sex-age specific cBED assay-based HIV incidence estimates ranged from 18% to 203%; in 13 of the 15 sex-age groups the CVs were larger than 25%; in 10 sex-age groups the CVs were larger than 50%; and in 4 sex-age groups they were larger than 100%.

In a rural community in South Africa, we found a long-term FPR of the cBED assay of 0.0169. This value is substantially lower than the two previous estimates of the ratio. The first estimate (0.0560) “was based on analysis of specimens from longer-term-infected individuals not known to have clinical AIDS, opportunistic infections, or to be on treatment” in the USA

Many previous studies have used the first estimate of the long-term FPR in their estimations of HIV incidence based on cross-sectional cBED assay surveys (e.g.

Our findings thus confirm the previous results by McDougal et al.

We further found that the different formulae to estimate HIV incidence based on the cBED assay results, did not produce significantly different values even though they differ in their underlying assumptions, suggesting that the choice of formula may not be very important for most practical purposes. Finally, we showed that the estimates of the long-term FPR based on data from a longitudinal HIV surveillance are very robust to changes in the definition of “long-term” (i.e. the choice of the maximum BED progression time).

Our longitudinal HIV incidence estimates in this article are slightly lower than previously published estimates from the same community

HIV incidence estimates by sex and age group are important for validating the cBED assay method as an approach to measure HIV incidence

The promise of the cBED assay for HIV surveillance, program evaluation and policy making, lies in the fact that it allows HIV incidence estimation from cross-sectional samples. Cross-sectional HIV status information, however, does not permit estimation of the long-term FPR, requiring researchers to obtain this parameter independently. It is thus important that the parameters necessary for HIV incidence estimation are calibrated using data from those settings where longitudinal follow-up is available. A meta-analysis of the long-term FPR of the cBED assay may help explain why the parameter estimates differ and allow the determination of valid regional parameter estimates.

It may further be necessary to measure the long-term FPR repeatedly over time. For instance, one of the reasons why people with non-recent HIV infections are falsely classified as recently infected by the cBED assay is viral suppression due to ART

An alternative to using the long-term FPR in order to adjust cBED assay-based HIV incidence estimates for the presence of people who are falsely classified as recently HIV-infected is to use additional information on time since seroconversion to identify these individuals and correct the misclassification. Information on time since seroconversion, which can be obtained in cross-sectional surveys, could be based on biological parameters that change with time since infection (such as CD4 count, total lymphocyte count, or viral load), clinical assessment (such as screening for HIV-related diseases that indicate late-stage HIV disease

In conclusion, our study demonstrates that without a locally measured long-term FPR HIV incidence estimates based on the cBED assay may be severely biased, but that the cBED assay performs well in HIV incidence estimation, if a locally appropriate long-term FPR is used.

We thank Kobus Herbst, Phumzile Dlamini, Thobeka Mngomezulu, Zanomsa Gqwede and the field staff at the Africa Centre for Health and Population Studies at the University of KwaZulu-Natal, South Africa, for their work in collecting the data used in this study and the communities in the Africa Centre demographic surveillance area for their support and participation in this study. We also thank Beverley Singh and Mahlaste Maleka for conducting the cBED immunoassay tests at the National Centre for Infectious Diseases in Johannesburg, South Africa. We thank the reviewers for helpful comments that led to improvement of the manuscript.