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Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion.

ABSTRACT: The serologic testing algorithm for recent HIV seroconversion (STARHS) calculates incidence using the proportion of testers who produce a level of HIV antibody high enough to be detected by ELISA but low enough to suggest recent infection. The validity of STARHS relies on independence between dates of HIV infection and dates of antibody testing. When subjects choose the time of their own test, testing may be motivated by risky behaviour or symptoms of infection and the criterion may not be met. This analysis was conducted to ascertain whether estimates of incidence derived using STARHS were consistent with estimates derived using a method more robust against motivated testing.A cohort-based incidence estimator and two STARHS methods were applied to identical populations (n=3821) tested for HIV antibody at publicly funded sites in Seattle. Overall seroincidence estimates, demographically stratified estimates and incidence rate ratios were compared across methods. The proportion of low-antibody testers among HIV-infected individuals was compared with the proportion expected given their testing histories.STARHS estimates generally exceeded cohort-based estimates. Incidence ratios derived using STARHS between demographic strata were not consistent across methods. The proportion of HIV-infected individuals with lower antibody levels exceeded that which would be expected under independence between infection and testing.Incidence estimates and incidence rate ratios derived using methods that rely on the changing antibody level over the course of HIV infection may be vulnerable to bias when applied to populations who choose the time of their own testing.

SUBMITTER: White E

PROVIDER: S-EPMC3414255 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

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Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion.

White Edward E Goldbaum Gary G Goodreau Steven S Lumley Thomas T Hawes Stephen E SE

Sexually transmitted infections 20100624 4

<h4>Objectives</h4>The serologic testing algorithm for recent HIV seroconversion (STARHS) calculates incidence using the proportion of testers who produce a level of HIV antibody high enough to be detected by ELISA but low enough to suggest recent infection. The validity of STARHS relies on independence between dates of HIV infection and dates of antibody testing. When subjects choose the time of their own test, testing may be motivated by risky behaviour or symptoms of infection and the criteri ...[more]

PMID: 20576914

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Project description:BackgroundSelection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results.MethodsThe identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated.ResultsUsing nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% - 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive.ConclusionsOur approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.

Project description:Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Dataset Information

Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion.

Publications

Interpopulation variation in HIV testing promptness may introduce bias in HIV incidence estimates using the serologic testing algorithm for recent HIV seroconversion.

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