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Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.

Hund L, Pagano M - Emerg Themes Epidemiol (2013)

Bottom Line: : Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing.For those individuals, we suggest offering the option of being tested in a pool.We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Family and Community Medicine, University of New Mexico, 2400 Tucker NE, Albuquerque, NM 87106, USA. lhund@salud.unm.edu.

ABSTRACT
: Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates.

No MeSH data available.


Related in: MedlinePlus

Percent bias in the combined estimator. Percent bias in the MLE estimator  (thin lines) and the Burrows estimator  (bold lines) for pool size k=7 as a function of sample size for low, moderate, and high prevalence settings. Using the Burrows estimator results in a substantial reduction in finite sample bias.
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Figure 3: Percent bias in the combined estimator. Percent bias in the MLE estimator (thin lines) and the Burrows estimator (bold lines) for pool size k=7 as a function of sample size for low, moderate, and high prevalence settings. Using the Burrows estimator results in a substantial reduction in finite sample bias.

Mentions: We can use the Burrows estimator to define a new prevalence estimator , which is constructed by substituting for in the combined estimator. This new estimator has much smaller finite sample bias than in small samples. In Figure 3, we plot the percent bias in the prevalence estimator for and for pool size k=7 (the size for which we see the greatest finite-sample bias). The original estimator always overestimates the prevalence, with the severity of the bias decreasing as the sample size increases. The Burrows estimator has negligible bias, even for sample sizes as small as 100. Consequently, we recommend using in practice rather than .


Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.

Hund L, Pagano M - Emerg Themes Epidemiol (2013)

Percent bias in the combined estimator. Percent bias in the MLE estimator  (thin lines) and the Burrows estimator  (bold lines) for pool size k=7 as a function of sample size for low, moderate, and high prevalence settings. Using the Burrows estimator results in a substantial reduction in finite sample bias.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3649931&req=5

Figure 3: Percent bias in the combined estimator. Percent bias in the MLE estimator (thin lines) and the Burrows estimator (bold lines) for pool size k=7 as a function of sample size for low, moderate, and high prevalence settings. Using the Burrows estimator results in a substantial reduction in finite sample bias.
Mentions: We can use the Burrows estimator to define a new prevalence estimator , which is constructed by substituting for in the combined estimator. This new estimator has much smaller finite sample bias than in small samples. In Figure 3, we plot the percent bias in the prevalence estimator for and for pool size k=7 (the size for which we see the greatest finite-sample bias). The original estimator always overestimates the prevalence, with the severity of the bias decreasing as the sample size increases. The Burrows estimator has negligible bias, even for sample sizes as small as 100. Consequently, we recommend using in practice rather than .

Bottom Line: : Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing.For those individuals, we suggest offering the option of being tested in a pool.We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Family and Community Medicine, University of New Mexico, 2400 Tucker NE, Albuquerque, NM 87106, USA. lhund@salud.unm.edu.

ABSTRACT
: Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates.

No MeSH data available.


Related in: MedlinePlus