Successful use of pooled sera to estimate HIV antibody seroprevalence and eliminate all positive cases

Prevalence of undiagnosed acute and chronic HIV in a lower-prevalence urban emergency department

OBJECTIVES

We estimated the seroprevalence of both acute and chronic HIV infection by using a random sample of emergency department (ED) patients from a region of the United States with low-to-moderate HIV prevalence.

METHODS

This cross-sectional seroprevalence study consecutively enrolled patients aged 18 to 64 years within randomly selected sampling blocks in a Midwestern urban ED in a region of lower HIV prevalence in 2008 to 2009. Participants were compensated for providing a blood sample and health information. After de-identification, we assayed samples for HIV antibody and nucleic acid.

RESULTS

There were 926 participants who consented and enrolled. Overall, prevalence of undiagnosed HIV was 0.76% (95% confidence interval [CI] = 0.30%, 1.56%). Three participants (0.32%; 95% CI = 0.09%, 0.86%) were nucleic acid-positive but antibody-negative and 4 (0.43%; 95% CI = 0.15%, 1.02%) were antibody-positive.

CONCLUSIONS

Even when the absolute prevalence is low, a considerable proportion of undetected HIV cases in an ED population are acute. Identification of acute HIV in ED settings should receive increased priority.

Randomized comparison of universal and targeted HIV screening in the emergency department

OBJECTIVE

Universal HIV screening is recommended but challenging to implement. Selectively targeting those at risk is thought to miss cases, but previous studies are limited by narrow risk criteria, incomplete implementation, and absence of direct comparisons. We hypothesized that targeted HIV screening, when fully implemented and using maximally broad risk criteria, could detect nearly as many cases as universal screening with many fewer tests.

METHODS

This single-center cluster-randomized trial compared universal and targeted patient selection for HIV screening in a lower prevalence urban emergency department. Patients were excluded for age (<18 and >64 years), known HIV infection, or previous approach for HIV testing that day. Targeted screening was offered for any risk indicator identified from charts, staff referral, or self-disclosure. Universal screening was offered regardless of risk. Baseline seroprevalence was estimated from consecutive deidentified blood samples.

RESULTS

There were 9572 eligible visits during which the patient was approached. For universal screening, 40.8% (1915/4692) consented with 6 being newly diagnosed [0.31%, 95% confidence interval (CI): 0.13% to 0.65%]. For targeted screening, 37% (1813/4880) had no testing indication. Of the 3067 remaining, 47.4% (1454) consented with 3 being newly diagnosed (0.22%, 95% CI: 0.06% to 0.55%). Estimated seroprevalence was 0.36% (95% CI: 0.16% to 0.70%). Targeted screening had a higher proportion consenting (47.4% vs. 40.8%, P < 0.002), but a lower proportion of ED encounters with testing (29.7% vs. 40.7%, P < 0.002).

CONCLUSIONS

Targeted screening, even when fully implemented with maximally permissive selection, offered no important increase in positivity rate or decrease in tests performed. Universal screening diagnosed more cases, because more were tested, despite a modestly lower consent rate.

Pooling Experiments for Blood Screening and Drug Discovery

Pooling experiments date as far back as 1915 and were initially used in dilution studies for estimating the density of organisms in some medium. These early uses of pooling were necessitated by scientific and technical limitations. Today, pooling experiments are driven by the potential cost savings and precision gains that can result, and they are making a substantial impact on blood screening and drug discovery. A general review of pooling experiments is given here, with additional details and discussion of issues and methods for two important application areas, namely, blood testing and drug discovery. The blood testing application is very old, from 1943, yet is still used today, especially for HIV antibody screening. In contrast, the drug discovery application is relatively new, with early uses occurring in the period from the late 1980s to early 1990s. Statistical methods for this latter application are still actively being investigated and developed through both the pharmaceutical industries and academic research. The ability of pooling to investigate synergism offers exciting prospects for the discovery of combination therapies.

Estimation of animal-level prevalence from pooled samples in animal production

Often, the prevalence of an infection in the animal-production sector is determined at the group level. The prevalence at animal level (p) gives more-precise information on the infection status of the sector. This paper shows that pooled-sample data together with mathematical models allow for estimation of p. For this, model assumptions have to be made on the variation of p between groups separated in space and/or time. Formulas were derived for four models that were based on different assumptions. Model 1 assumed that p has the same value for all groups. Models 2-4 assumed that some of the groups were not infected. In addition, model 2 assumed that p has the same value for all infected groups; model 3 assumed that for an infected group, p is equal to either p(1) or p(2); and model 4 assumed that p was Beta distributed among infected groups. The models were applied to data sets on Salmonella infection in broiler flocks, including serotype data dominated by S. Hadar and S. Paratyphi B, var. Java. Based on likelihood-ratio tests, models 3 and 4 consistently fitted significantly better to the data. The applicability of model 4 is numerically bounded, related to the shape of the Beta distribution of p. Model calculations show that flock-level prevalence of Salmonella is much higher after than before slaughter. This difference (which possibly is related to different types of samples) is much smaller at the animal level. An important result of the estimation of p is that it in turn allows for an estimation of the proportion of false-negative groups--which is important in estimating the effect of veterinary or public-health measures.

Estimating the prevalence of infectious agents using pooled samples: biometrical considerations

Pooled testing of units is a common approach in the prevalence estimation of infectious agents, which leads to a reduction of total costs of diagnostic testing. We examine how the pool size affects the statistical properties of the prevalence estimator r. Exact formulae are used to determine bias and precision of r. It is shown that with moderate pool sizes the (upward) bias of r is negligible. If there is no diagnostic error, the random error of r increases slightly with higher pool sizes, whereas if sensitivity and specificity are lower than 1, pooling may markedly decrease the random error of r. Another reason why pooling may be beneficial (and even indispensable) is that it greatly reduces the huge bias that can result if the assumed values of the sensitivity and specificity of the diagnostic test are not equal to the true values. The numerical calculations show that, in case of prevalence rates of up to 5% and total sample sizes of n > or = 500, pool sizes of about 10 to 20 are generally satisfactory from a statistical view-point. The methodological advantages and disadvantages of more complicated pooling strategies involving repeated testing of units are discussed.